Classification of bladder cancer based on immune cell infiltration and construction of a risk prediction model for prognosis / 浙江大学学报·医学版

OBJECTIVES@#To classify bladder cancer based on immune cell infiltration score and to construct a risk assessment model for prognosis of patients.@*METHODS@#The transcriptome data and data of breast cancer patients were obtained from the TCGA database. The single sample gene set enrichment analysis was used to calculate the infiltration scores of 16 immune cells. The classification of breast cancer patients was realized by unsupervised clustering, and the sensitivity of patients with different types to immunotherapy and chemotherapy was analyzed. The key modules significantly related to the infiltration of key immune cells were identified by weighted correlation network analysis (WGCNA), and the key genes in the modules were extracted. A risk scoring model and a nomogram for risk assessment of prognosis for bladder cancer patients were constructed and verified.@*RESULTS@#The immune cell infiltration scores of normal tissues and tumor tissues were calculated, and B cells, mast cells, neutrophils, T helper cells and tumor infiltrating lymphocytes were determined to be the key immune cells of bladder cancer. Breast cancer patients were clustered into two groups (Cluster 1 and Custer 2) based on immune cell infiltration scores. Compared with patients with Cluster 1, patients with Cluster 2 were more likely to benefit from immunotherapy (P<0.05), and patients with Cluster 2 were more sensitive to Enbeaten, Docetaxel, Cyclopamine, and Akadixin (P<0.05). WGCNA screened out 35 genes related to key immune cells, and 4 genes (GPR171, HOXB3, HOXB5 and HOXB6) related to the prognosis of bladder cancer were further screened by LASSO Cox regression. The areas under the ROC curve (AUC) of the bladder cancer prognosis risk scoring model based on these 4 genes to predict the 1-, 3- and 5-year survival of patients were 0.735, 0.765 and 0.799, respectively. The nomogram constructed by combining risk score and clinical parameters has high accuracy in predicting the 1-, 3-, and 5-year overall survival of bladder cancer patients.@*CONCLUSIONS@#According to the immune cell infiltration score, bladder cancer patients can be classified. And the bladder cancer prognosis risk scoring model and nomogram based on key immune cell-related genes have high accuracy in predicting the prognosis of bladder cancer patients.

Risk factors for ketoacidosis in children/adolescents with type 1 diabetes mellitus and establishment of a predictive model / 中国当代儿科杂志

OBJECTIVES@#To investigate the risk factors for diabetic ketoacidosis (DKA) in children/adolescents with type 1 diabetes mellitus (T1DM) and to establish a model for predicting the risk of DKA.@*METHODS@#A retrospective analysis was performed on 217 children/adolescents with T1DM who were admitted to General Hospital of Ningxia Medical University from January 2018 to December 2021. Among the 217 children/adolescents,169 cases with DKA were included as the DKA group and 48 cases without DKA were included as the non-DKA group. The risk factors for DKA in the children/adolescents with T1DM were analyzed, and a nomogram model was established for predicting the risk of DKA in children/adolescents with T1DM.@*RESULTS@#For the 217 children/adolescents with T1DM, the incidence rate of DKA was 77.9% (169/217). The multivariate logistic regression analysis showed that high levels of random blood glucose, hemoglobin A1c (HbA1c), blood ketone body, and triglyceride on admission were closely associated with the development of DKA in the children/adolescents with T1DM (OR=1.156, 3.2031015, 20.131, and 9.519 respectively; P<0.05). The nomogram prediction model had a C-statistic of 0.95, with a mean absolute error of 0.004 between the risk of DKA predicted by the nomogram model and the actual risk of DKA, indicating that the model had a good overall prediction ability.@*CONCLUSIONS@#High levels of random blood glucose, HbA1c, blood ketone body, and triglyceride on admission are closely associated with the development of DKA in children/adolescents with T1DM, and targeted intervention measures should be developed to reduce the risk of DKA.

Establishment of a Predictive Model for Chronic Cough after Pulmonary Resection / 中国肺癌杂志

BACKGROUND@#Chronic cough after pulmonary resection is one of the most common complications, which seriously affects the quality of life of patients after surgery. Therefore, the aim of this study is to explore the risk factors of chronic cough after pulmonary resection and construct a prediction model.@*METHODS@#The clinical data and postoperative cough of 499 patients who underwent pneumonectomy or pulmonary resection in The First Affiliated Hospital of University of Science and Technology of China from January 2021 to June 2023 were retrospectively analyzed. The patients were randomly divided into training set (n=348) and validation set (n=151) according to the principle of 7:3 randomization. According to whether the patients in the training set had chronic cough after surgery, they were divided into cough group and non-cough group. The Mandarin Chinese version of Leicester cough questionnare (LCQ-MC) was used to assess the severity of cough and its impact on patients' quality of life before and after surgery. The visual analog scale (VAS) and the self-designed numerical rating scale (NRS) were used to evaluate the postoperative chronic cough. Univariate and multivariate Logistic regression analysis were used to analyze the independent risk factors and construct a model. Receiver operator characteristic (ROC) curve was used to evaluate the discrimination of the model, and calibration curve was used to evaluate the consistency of the model. The clinical application value of the model was evaluated by decision curve analysis (DCA).@*RESULTS@#Multivariate Logistic analysis screened out that preoperative forced expiratory volume in the first second/forced vital capacity (FEV1/FVC), surgical procedure, upper mediastinal lymph node dissection, subcarinal lymph node dissection, and postoperative closed thoracic drainage time were independent risk factors for postoperative chronic cough. Based on the results of multivariate analysis, a Nomogram prediction model was constructed. The area under the ROC curve was 0.954 (95%CI: 0.930-0.978), and the cut-off value corresponding to the maximum Youden index was 0.171, with a sensitivity of 94.7% and a specificity of 86.6%. With a Bootstrap sample of 1000 times, the predicted risk of chronic cough after pulmonary resection by the calibration curve was highly consistent with the actual risk. DCA showed that when the preprobability of the prediction model probability was between 0.1 and 0.9, patients showed a positive net benefit.@*CONCLUSIONS@#Chronic cough after pulmonary resection seriously affects the quality of life of patients. The visual presentation form of the Nomogram is helpful to accurately predict chronic cough after pulmonary resection and provide support for clinical decision-making.

Establishment and validation of nomogram prediction model of cefoperazone/sulbactam-induced thrombocytopenia / 中国药房

OBJECTIVE To explore the predictive factors of cefoperazone/sulbactam-induced thrombocytopenia in adult inpatients， and to establish and validate the nomogram prediction model. METHODS Data of adult inpatients treated with cefoperazone/sulbactam in Xi’an Central Hospital from Jun. 30th， 2021 to Jun. 30th， 2023 were retrospectively collected. The training set and internal validation set were randomly constructed in a 7∶3 ratio. Singler factor and multifactor Logistic regression analysis were used to screen the independent predictors of cefoperazone/sulbactam-induced thrombocytopenia. The nomogram was drawn by using “RMS” of R 4.0.3 software， and the predictive performance of the model was evaluated by the receiver operating characteristic curve and C-index curve. Hosmer-Lemeshow goodness-of-fit test was used to evaluate the calibration degree of the model. Using the same standard， the clinical data of hospitalized patients receiving cefoperazone/sulbactam in Xi’an First Hospital in the same period were collected for external validation of the nomogram prediction model. RESULTS A total of 1 045 patients in Xi’an Central Hospital were included in this study， among which 67 patients suffered from cefoperazone/sulbactam-induced thrombocytopenia， with an incidence of 6.41%. After the false positive patients were excluded， 473 patients were included finally， including 331 in the training set and 142 in theinternal validation set. Multifactor Logistic regression analysis showed that age [OR＝1.043， 95%CI （1.017， 1.070）]， estimated glomerular filtration rate （eGFR） [OR＝0.988，95%CI（0.977， 0.998）]， baseline platelet （PLT） [OR＝0.989， 95%CI（0.982， 0.996）]， nutritional risk [OR＝3.863， 95%CI（1.884， 7.921）] and cumulative defined daily doses （DDDs） [OR＝1.082， 95%CI（1.020， 1.147）] were independent predictors for cefoperazone/sulbactam-induced thrombocytopenia （P＜0.05）. The C-index values of the training set and the internal validation set were 0.824 [95%CI （0.759， 0.890）] and 0.828 [95%CI （0.749， 0.933）]， respectively. The results of the Hosmer-Lemeshow test showed that χ 2 values were 0.441 （P＝0.802） and 1.804 （P＝0.406）. In the external validation set， the C-index value was 0.808 [95%CI （0.672， 0.945）]， the χ 2 value of the Hosmer-Lemeshow test was 0.899 （P＝0.638）. CONCLUSIONS The independent predictors of cefoperazone/sulbactam-induced thrombocytopenia include age， baseline PLT， eGFR， nutritional risk and cumulative DDDs. The model has good predictive efficacy and extrapolation ability， which can help clinic identify the potential risk of cefoperazone/sulbactam-induced thrombocytopenia quickly and accurately.

Nomogram including serum ferritin to predict the occurrence of diabetic retinopathy / 国际眼科杂志(Guoji Yanke Zazhi)

AIM:To establish a nomogram model to predict the effect of serum ferritin on diabetic retinopathy and evaluate the model.METHODS:A total of 21 variables, including ferritin, were screened by univariate and multivariate regression analysis to determine the risk factors of diabetic retinopathy. A nomogram prediction model was established for evaluation and calibration.RESULTS:Ferritin, duration of diabetes, hemoglobin, urine microalbumin, regularity of medication and body mass index were included in the nomogram model. The consistency index of the prediction model with serum ferritin was 0.813(95%CI: 0.748-0.879). The calibration curves of internal and external verification showed good performance, and the probability of the threshold suggested by the decision curve was in the range 10% to 90%. The model had a high net profit value.CONCLUSIONS:Serum ferritin is an important risk factor for diabetic retinopathy. A new nomogram model, which includes body mass index, duration of diabetes, ferritin, hemoglobin, urine microalbumin and regularity of medication, has a high predictive accuracy and could provide early prediction for clinicians.

Construction of a column-line graph for predicting the probability of survival in early-stage elderly HER2-positive breast cancer based on the SEER database / 中国临床药理学与治疗学

AIM: To construct column-line plots to predict survival in elderly patients with early-stage HER2-positive breast cancer using the Surveillance, Epidemiology and End Results (SEER) database. METHODS: 5 220 (based on the era of single-targeted therapy) and 1 176 (based on the era of dual-targeted therapy) patients screened in the SEER database were randomized into a training group and an internal validation group. COX proportional risk regression was used to screen survival-related predictors and build a column-line graphical model, and the accuracy and utility of the model were tested using the consistency index (C-index), calibration curves, and time-dependent ROC curves. Patients receiving chemotherapy and non-chemotherapy were statistically paired using two-group propensity score matching, and subgroup analyses were performed on the screened variables. RESULTS: The single-targeted therapy era line graph was constructed from seven variables: age, marital status, T-stage, N-stage, surgery, chemotherapy, and radiotherapy. The dual-targeted therapy era line graph was constructed from five variables: age, AJCC staging, surgery, chemotherapy, and radiotherapy. The results of the subgroup analysis showed that older HER2-positive breast cancer patients who received chemotherapy had better OS. CONCLUSION: Based on the SEER database, an accurate column-line graph predicting survival in elderly patients with early-stage HER2-positive breast cancer was established and validated. This study suggests that chemotherapy increases survival benefit in elderly patients.

A predictive model of lymph node metastasis after thoracoscopic surgery for lung adenocarcinoma with a diameter≤3 cm / 中国胸心血管外科临床杂志

@#Objective    To predict the probability of lymph node metastasis after thoracoscopic surgery in patients with lung adenocarcinoma based on nomogram. Methods    We analyzed the clinical data of the patients with lung adenocarcinoma treated in the department of thoracic surgery of our hospital from June 2018 to May 2021. The patients were randomly divided into a training group and a validation group. The variables that may affect the lymph node metastasis of lung adenocarcinoma were screened out by univariate logistic regression, and then the clinical prediction model was constructed by multivariate logistic regression. The nomogram was used to show the model visually, the receiver operating characteristic (ROC) curve, calibration curve and clinical decision curve to evaluate the calibration degree and practicability of the model. Results    Finally 249 patients were collected, including 117 males aged 53.15±13.95 years and 132 females aged 47.36±13.10 years. There were 180 patients in the training group, and 69 patients in the validation group. There was a significant correlation between the 6 clinicopathological characteristics and lymph node metastasis of lung adenocarcinoma in the univariate logistic regression. The area under the ROC curve in the training group was 0.863, suggesting the ability to distinguish lymph node metastasis, which was confirmed in the validation group (area under the ROC curve was 0.847). The nomogram and clinical decision curve also performed well in the follow-up analysis, which proved its potential clinical value. Conclusion    This study provides a nomogram combined with clinicopathological characteristics, which can be used to predict the risk of lymph node metastasis in patients with lung adenocarcinoma with a diameter≤3 cm.

Construction of a individualized model for predicting the risk of recurrence in patients with retinal vein occlusion induced macular edema / 国际眼科杂志(Guoji Yanke Zazhi)

AIM: To analyze the recurrence factors of patients with retinal vein occlusion(RVO)induced macular edema(ME)and construct a nomogram model.METHODS: Retrospective study. A total of 306 patients with RVO induced ME admitted to our hospital from January 2019 to June 2022 were included as study objects, and they were divided into modeling group with 214 cases(214 eyes)and 92 cases(92 eyes)in the verification group by 7:3. All patients were followed up for 1 a after receiving anti-vascular endothelial growth factor(VEGF)treatment, and patients in the modeling group were separated into a recurrence group(n=66)and a non recurrence group(n=148)based on whether they had recurrence. Clinical data were collected and multivariate Logistic regression was applied to analyze and determine the factors affecting recurrence in patients with RVO induced ME; R3.6.3 software was applied to construct a nomogram model for predicting the recurrence risk of patients with RVO induced ME; ROC curve and calibration curve were applied to evaluate the discrimination and consistency of nomogram model in predicting the recurrence risk of patients with RVO induced ME.RESULTS: There were statistically significant differences in central retinal thickness(CRT), course of disease, hyperreflective foci(HF), disorder of retinal inner layer structure, and injection frequency between the non recurrence group and the recurrence group before treatment(all P&#x0026;#x003C;0.05). The multivariate Logistic regression analysis showed that pre-treatment CRT(OR=1.011), course of disease(OR=1.104), HF(OR=5.074), retinal inner layer structural disorder(OR=4.640), and injection frequency(OR=4.036)were influencing factors for recurrence in patients with RVO induced ME(all P&#x0026;#x003C;0.01). The area under the ROC curve of the modeling group was 0.924(95%CI: 0.882-0.966), the slope of the calibration curve was close to 1, and the results of the Hosmer-Lemeshow goodness of fit test showed that χ2=11.817, P=0.160; the area under the ROC curve of the verification group was 0.939(95%CI: 0.892-0.985), the slope of the calibration curve was close to 1, and the results of the Hosmer-Lemeshow goodness of fit test showed χ2=6.082, P=0.638.CONCLUSION: Pre-treatment CRT, course of disease, HF, disorder of retinal inner layer structure, and injection frequency are independent risk factors for recurrence in patients with RVO induced ME. The nomogram model constructed based on this has a high discrimination and consistency in predicting the recurrence risk of patients with RVO induced ME.

Construction and validation of a prediction model for public acceptance of kidney xenotransplantation in China / 器官移植

Objective To explore the public attitude towards kidney xenotransplantation in China by constructing and validating the prediction model based on xenotransplantation questionnaire. Methods A convenient sampling survey was conducted among the public in China with the platform of Wenjuanxing to analyze public acceptance of kidney xenotransplantation and influencing factors. Using random distribution method, all included questionnaires (n=2 280) were divided into the training and validation sets according to a ratio of 7:3. A prediction model was constructed and validated. Results A total of 2 280 questionnaires were included. The public acceptance rate of xenotransplantation was 71.3%. Multivariate analysis showed that gender, marital status, resident area, medical insurance coverage, religious belief, vegetarianism, awareness of kidney xenotransplantation and whether on the waiting list for kidney transplantation were the independent influencing factors for public acceptance of kidney xenotransplantation (all P<0.05). The area under the curve (AUC) of receiver operating characteristic (ROC) of the prediction model in the training set was 0.773, and 0.785 in the validation set. The calibration curves in the training and validation sets indicated that the prediction models yielded good prediction value. Decision curve analysis (DCA) suggested that the prediction efficiency of the model was high. Conclusions In China, public acceptance of kidney xenotransplantation is relatively high, whereas it remains to be significantly enhanced. The prediction model based on questionnaire survey has favorable prediction efficiency, which provides reference for subsequent research.

Comprehensive analysis of clinicopathologic and sonographic features in thyroid cancer with skip lymph node metastasis: establish and assessment of a prediction nomogram

Abstract Lateral Lymph Node Metastasis (LLNM) is common in Papillary Thyroid Carcinoma (PTC) and is associated with a poor prognosis. LLNM without central lymph node metastasis as skip metastasis is not common. We aimed to investigate clinicopathologic and sonographic risk factors for skip metastasis in PTC patients, and to establish a nomogram for predicting the possibility of skip metastasis in order to determine the therapeutic strategy. We retrospectively reviewed the data of 1037 PTC patients who underwent surgery from 2016 to 2020 at a single institution. Univariate and multivariate analyses were used to identify the clinicopathologic and preoperative sonographic risk factors of skip metastasis. A nomogram including the risk factors for predicting skip metastasis was further developed and validated. The incidence of skip metastasis was 10.7%. The univariate and multivariate analyses suggested that gender (p = 0.001), tumor location (p = 0.000), extrathyroidal extension (p = 0.000), and calcification (p = 0.000) were independent risk factors. For papillary thyroid microcarcinoma, tumor location (p = 0.000) and calcification (p = 0.001) were independent risk factors. A nomogram according to the clinicopathologic and sonographic predictors was developed. The receiver operating characteristic curve indicated that AUC was 0.824 and had an excellent consistency. The calibration plot analysis showed a good performance and clinical utility of the model. Decision curve analysis revealed it was clinically useful. A nomogram for predicting the probability of skip metastasis was developed, which exhibited a favorable predictive value and consistency. For the female PTC patient, tumor located at the upper pole is more likely to have skip metastasis. Surgeons and sonographers should pay close attention to the patients who have the risk factors. Evidence level: This article's evidence level is 3. Level 3 evidence is derived from nonrandomized, controlled clinical trials. In this study, patients who receive an intervention are compared to a control group. Authors may detect a statistically significant and clinically relevant outcome.

Impact of crossplay between ocular aberrations and depth of focus in topo-guided laser-assisted in situ keratomileusis outcomes

Purpose: To develop a nomogram in cases with mismatch between subjective and Topolyzer cylinder, and based on the magnitude of the mismatch, customize a treatment plan to attain good visual outcomes post?laser?assisted in situ keratomileusis (LASIK) surgery. Methods: The patients were evaluated preoperatively using corneal tomography with Pentacam. Five optimal corneal topography scans were obtained from the Topolyzer Vario were used for planning the LASIK treatment. For the nomogram purpose, the patients were divided into three categories based on the difference between the subjective cylinder and Topolyzer (corneal) cylinder. The first group (group 1) consisted of eyes of patients, where the difference was less than or equal to 0.4 D. The second group (group 2) consisted of eyes, where the difference was more than 0.4 D and the subjective cylinder was lesser than the Topolyzer cylinder. The third group (group 3) included eyes where the difference was more than 0.4 D but the subjective cylinder was greater than the Topolyzer cylinder. LASIK was performed with the WaveLight FS 200 femtosecond laser and WaveLight EX500 excimer laser. Assessment of astigmatism correction for the three groups was done using Aplins vector analysis. For comparison of proportions, Chi?square test was used. A P value less than 0.05 was considered statistically significant. Results: The UDVA was statistically significantly different when compared between groups 1 and 2 (P = 0.02). However, the corrected distance visual acuity (CDVA) was similar among all the three groups (P = 0.1). Group 3 showed an increase of residual cylinder by ?0.25 D, which was significant at intermediate and near reading distances (P < 0.05). Group 3 showed significantly higher target?induced astigmatism (TIA) compared to groups 1 and 2 (P = 0.01). The mean surgically induced astigmatism (SIA) was the least in group 2, which was statistically significant (P < 0.01). Conclusion: The outcomes for distance vision using our nomogram postoperatively were excellent, but further refinement for improving the near vision outcomes is required

Risk factors for neonatal asphyxia and establishment of a nomogram model for predicting neonatal asphyxia in Hubei Enshi Tujia and Miao Autonomous Prefecture: a multicenter study / 中国当代儿科杂志

OBJECTIVES@#To investigate the risk factors for neonatal asphyxia in Hubei Enshi Tujia and Miao Autonomous Prefecture and establish a nomogram model for predicting the risk of neonatal asphyxia.@*METHODS@#A retrospective study was conducted with 613 cases of neonatal asphyxia treated in 20 cooperative hospitals in Enshi Tujia and Miao Autonomous Prefecture from January to December 2019 as the asphyxia group, and 988 randomly selected non-asphyxia neonates born and admitted to the neonatology department of these hospitals during the same period as the control group. Univariate and multivariate analyses were used to identify risk factors for neonatal asphyxia. R software (4.2.2) was used to establish a nomogram model. Receiver operator characteristic curve, calibration curve, and decision curve analysis were used to assess the discrimination, calibration, and clinical usefulness of the model for predicting the risk of neonatal asphyxia, respectively.@*RESULTS@#Multivariate logistic regression analysis showed that minority (Tujia), male sex, premature birth, congenital malformations, abnormal fetal position, intrauterine distress, maternal occupation as a farmer, education level below high school, fewer than 9 prenatal check-ups, threatened abortion, abnormal umbilical cord, abnormal amniotic fluid, placenta previa, abruptio placentae, emergency caesarean section, and assisted delivery were independent risk factors for neonatal asphyxia (P<0.05). The area under the curve of the model for predicting the risk of neonatal asphyxia based on these risk factors was 0.748 (95%CI: 0.723-0.772). The calibration curve indicated high accuracy of the model for predicting the risk of neonatal asphyxia. The decision curve analysis showed that the model could provide a higher net benefit for neonates at risk of asphyxia.@*CONCLUSIONS@#The risk factors for neonatal asphyxia in Hubei Enshi Tujia and Miao Autonomous Prefecture are multifactorial, and the nomogram model based on these factors has good value in predicting the risk of neonatal asphyxia, which can help clinicians identify neonates at high risk of asphyxia early, and reduce the incidence of neonatal asphyxia.

Visual and quantitative assessment of the effectiveness of non-vascularized bone grafting in osteonecrosis of the femoral head via CT-based radiomics and clinical data / 中国修复重建外科杂志

OBJECTIVE@#To investigate the value of CT-based radiomics and clinical data in predicting the efficacy of non-vascularized bone grafting (NVBG) in hip preservation, and to construct a visual, quantifiable, and effective method for decision-making of hip preservation.@*METHODS@#Between June 2009 and June 2019, 153 patients (182 hips) with osteonecrosis of the femoral head (ONFH) who underwent NVBG for hip preservation were included, and the training and testing sets were divided in a 7∶3 ratio to define hip preservation success or failure according to the 3-year postoperative follow-up. The radiomic features of the region of interest in the CT images were extracted, and the radiomics-scores were calculated by the linear weighting and coefficients of the radiomic features after dimensionality reduction. The clinical predictors were screened using univariate and multivariate Cox regression analysis. The radiomics model, clinical model, and clinical-radiomics (C-R) model were constructed respectively. Their predictive performance for the efficacy of hip preservation was compared in the training and testing sets, with evaluation indexes including area under the curve, C-Index, sensitivity, specificity, and calibration curve, etc. The best model was visualised using nomogram, and its clinical utility was assessed by decision curves.@*RESULTS@#At the 3-year postoperative follow-up, the cumulative survival rate of hip preservation was 70.33%. Continued exposure to risk factors postoperative and Japanese Investigation Committee (JIC) staging were clinical predictors of the efficacy of hip preservation, and 13 radiomic features derived from least absolute shrinkage and selection operator downscaling were used to calculate Rad-scores. The C-R model outperformed both the clinical and radiomics models in predicting the efficacy of hip preservation 1, 2, 3 years postoperative in both the training and testing sets ( P<0.05), with good agreement between the predicted and observed values. A nomogram constructed based on the C-R model showed that patients with lower Rad-scores, no further postoperative exposure to risk factors, and B or C1 types of JIC staging had a higher probability of femoral survival at 1, 2, 3 years postoperatively. The decision curve analysis showed that the C-R model had a higher total net benefit than both the clinical and radiomics models with a single predictor, and it could bring more net benefit to patients within a larger probability threshold.@*CONCLUSION@#The prediction model and nomogram constructed by CT-based radiomics combined with clinical data is a visual, quantifiable, and effective method for decision-making of hip preservation, which can predict the efficacy of NVBG before surgery and has a high value of clinical application.

Nomogram-Based Prediction of Risk of Cervical Lymph Node Metastasis in Differentiated Thyroid Carcinoma / 中国医学科学院学报

Objective To establish a nomogram for predicting the risk of cervical lymph node metastasis in differentiated thyroid carcinoma (DTC). Methods The patients with complete clinical data of DTC and cervical lymph node ultrasound and diagnosed based on pathological evidence from January 2019 to December 2021 were assigned into a training group (n=444) and a validation group (n=125).Lasso regression was performed to screen the data with differences between groups,and multivariate Logistic regression to establish a prediction model with the factors screened out by Lasso regression.C-index and calibration chart were employed to evaluate the prediction performance of the established model. Results The predictive factors for establishing the model were lymph node short diameter≥0.5 cm,long-to-short-axis ratio<2,disappearance of lymph node hilum,cystic transformation,hyperechogenicity,calcification,and abnormal blood flow (all P<0.001).The established model demonstrated a good discriminative ability,with the C index of 0.938 (95%CI=0.926-0.961) in the training group. Conclusion The nomogram established based on the ultrasound image features of cervical lymph nodes in DTC can accurately predict the risk of cervical lymph node metastasis in DTC.

Risk prediction of low birth weight infants in Shanghai / 上海预防医学

ObjectiveTo investigate the risk factors of fertility behaviors with preterm birth and low birth weight， and to develop a nomogram model to predict the occurrence of low birth weight. MethodsBirth registration information in Shanghai from 2010 to 2020 was collected， and ANOVA and Chi-square tests were used to compare the differences in reproductive behavior factors and newborn health status across time. The odds ratio （OR） value and 95%CI were calculated by a multi-classification logistic regression model to determine the association between reproductive behavior factors and preterm birth or low birth weight infants. A nomogram model was established based on logistic model and the area under the ROC curve was used to assess the effect of the model. ResultsThis analysis included 2 089 384 live newborns. The incidence of full-term low birth weight， preterm normal weight and preterm low birth weight in Shanghai was 0.94%， 2.48% and 2.01%， respectively. From 2010 to 2020， 40.00% women had a history of abortion， the proportion of women who gave birth at age ≥40 years old increased from 1.05% to 2.24%， the proportion of fathers aged ≥40 years increased from 4.79% to 7.48%， and the proportion of women with postgraduate or above increased from 4.81% to 11.74%. The incidence of preterm low birth weight in Shanghai showed an increasing trend over time. Logistic regression analysis showed that the risk of preterm low birth weight was lower in female than in male infants （OR=0.97， 95%CI： 0.95‒0.98）， and the risk of full-term low birth weight was higher than in male infants （OR=1.85， 95%CI： 1.80‒1.90）. The risk of preterm birth and low birth weight was lower for couples of childbearing age with higher education. The risk of preterm low birth weight in newborns tended to increase with maternal age at childbirth >30 years， paternal age ≥40 years， and the number of abortions >2 times. Mother <25 or >35 years， father aged 30‒34 years， and the number of abortions >3 times were the risk factors of full-term low birth weight infants. ConclusionCouples of childbearing age who choose to have children at too high or too low age may increase the risk of preterm birth or low birth weight， so it is necessary to strengthen population awareness and promote age-appropriate childbirth. Multiple abortions are also associated with preterm birth and low birth weight， and it is advisable to popularize the scientific knowledge of contraception and birth control to reduce unnecessary abortions. The nomogram in the study can visualize the risk of full-term and low birth weight infant at different levels of factors， which can assist couples preparing for pregnancy in making decisions about the timing of childbirth and understanding the level of risk.

Analysis of risk factors for neonatal preterm birth and construction of nomogram prediction model / 中国热带医学

@#Abstract: Objective To analyze the risk factors for neonatal preterm birth in 12 hospitals in Yunnan Province from 2016 to 2017, and to establish a nomogram prediction model for neonatal preterm birth, providing scientific evidence for the prevention of preterm birth. Methods A total of 20 445 pregnant women who gave birth in 12 hospitals in Yunnan Province from 2016 to 2017 were collected and grouped into a preterm group (n=1 186) and a full-term group (n=19 259) according to whether they had a premature delivery. The general information questionnaire of pregnant women designed by the research team was applied to understand the basic conditions and pregnancy information of the two groups, and the risk factors of preterm birth were determined by logistic regression analysis, R software was applied to draw a nomogram prediction model of neonatal preterm birth, and its predictive performance was tested. Results There were significant differences in the proportions of twins and above (9.11% vs 7.10%), pregnancy-induced hypertension (21.67% vs 18.57%), gestational diabetes mellitus (18.21% vs 15.90%), anemia (24.28% vs 20.70%), premature rupture of membranes (11.64% vs 9.76%), and abnormal placenta (7.08% vs 5.51%) between the preterm group and the full-term group (χ2=6.731, 7.055, 4.441, 8.691, 4.437, 5.232, all P<0.05); the logistic regression analysis showed that the risk factors for neonatal preterm birth were twins and above (OR=2.378), pregnancy-induced hypertension (OR=2.039), gestational diabetes mellitus (OR=1.824), anemia (OR=1.825), and premature rupture of membranes (OR=2.313) (all P<0.05); the discrimination (area under the curve was 0.794, 95%CI=0.738-0.850) and precision (goodness of fit HL test, χ2=8.864, P=0.312) of the nomogram model constructed to predict the occurrence of neonatal preterm birth were both good. Conclusions The nomogram model for preterm birth constructed based on 5 factors including number of fetuses, pregnancy-induced hypertension, gestational diabetes mellitus, anemia and premature rupture of membranes can predict the occurrence of neonatal preterm birth well, thus providing reference for the prevention of neonatal preterm birth.

Construction and evaluation of an artificial intelligence-based risk prediction model for death in patients with nasopharyngeal cancer / 南方医科大学学报

OBJECTIVE@#To screen the risk factors for death in patients with nasopharyngeal carcinoma (NPC) using artificial intelligence (AI) technology and establish a risk prediction model.@*METHODS@#The clinical data of NPC patients obtained from SEER database (1973-2015). The patients were randomly divided into model building and verification group at a 7∶3 ratio. Based on the data in the model building group, R software was used to identify the risk factors for death in NPC patients using 4 AI algorithms, namely eXtreme Gradient Boosting (XGBoost), Decision Tree (DT), Least absolute shrinkage and selection operator (LASSO) and random forest (RF), and a risk prediction model was constructed based on the risk factor identified. The C-Index, decision curve analysis (DCA), receiver operating characteristic (ROC) curve and calibration curve (CC) were used for internal validation of the model; the data in the validation group and clinical data of 96 NPC patients (collected from First Affiliated Hospital of Bengbu Medical College) were used for internal and external validation of the model.@*RESULTS@#The clinical data of a total of 2116 NPC patients were included (1484 in model building group and 632 in verification group). Risk factor screening showed that age, race, gender, stage M, stage T, and stage N were all risk factors of death in NPC patients. The risk prediction model for NPC-related death constructed based on these factors had a C-index of 0.76 for internal evaluation, an AUC of 0.74 and a net benefit rate of DCA of 9%-93%. The C-index of the model in internal verification was 0.740 with an AUC of 0.749 and a net benefit rate of DCA of 3%-89%, suggesting a high consistency of the two calibration curves. In external verification, the C-index of this model was 0.943 with a net benefit rate of DCA of 3%-97% and an AUC of 0.851, and the predicted value was consistent with the actual value.@*CONCLUSIONS@#Gender, age, race and TNM stage are risk factors of death of NPC patients, and the risk prediction model based on these factors can accurately predict the risks of death in NPC patients.

Establishment and validation of a nomogram for predicting prognosis of gastric neuroendocrine neoplasms based on data from 490 cases in a single center / 南方医科大学学报

OBJECTIVE@#To develop and validate a nomogram for predicting outcomes of patients with gastric neuroendocrine neoplasms (G-NENs).@*METHODS@#We retrospectively collected the clinical data from 490 patients with the diagnosis of G-NEN at our medical center from 2000 to 2021. Log-rank test was used to analyze the overall survival (OS) of the patients. The independent risk factors affecting the prognosis of G-NEN were identified by Cox regression analysis to construct the prognostic nomogram, whose performance was evaluated using the C-index, receiver-operating characteristic (ROC) curve, area under the ROC curve (AUC), calibration curve, DCA, and AUDC.@*RESULTS@#Among the 490 G-NEN patients (mean age of 58.6±10.92 years, including 346 male and 144 female patients), 130 (26.5%) had NET G1, 54 (11.0%) had NET G2, 206 (42.0%) had NEC, and 100 (20.5%) had MiNEN. None of the patients had NET G3. The numbers of patients in stage Ⅰ-Ⅳ were 222 (45.3%), 75 (15.3%), 130 (26.5%), and 63 (12.9%), respectively. Univariate and multivariate analyses identified age, pathological grade, tumor location, depth of invasion, lymph node metastasis, distant metastasis, and F-NLR as independent risk factors affecting the survival of the patients (P < 0.05). The C-index of the prognostic nomogram was 0.829 (95% CI: 0.800-0.858), and its AUC for predicting 1-, 3- and 5-year OS were 0.883, 0.895 and 0.944, respectively. The calibration curve confirmed a good consistency between the model prediction results and the actual observations. For predicting 1-year, 3-year and 5-year OS, the TNM staging system and the nomogram had AUC of 0.033 vs 0.0218, 0.191 vs 0.148, and 0.248 vs 0.197, respectively, suggesting higher net benefit and better clinical utility of the nomogram.@*CONCLUSION@#The prognostic nomogram established in this study has good predictive performance and clinical value to facilitate prognostic evaluation of individual patients with G-NEN.

Advances in post-operative prognostic models for hepatocellular carcinoma / 浙江大学学报（英文版）（B辑：生物医学和生物技术）

Hepatocellular carcinoma (HCC) is one of the most common malignancies and a leading cause of cancer-related death worldwide. Surgery remains the primary and most successful therapy option for the treatment of early- and mid-stage HCCs, but the high heterogeneity of HCC renders prognostic prediction challenging. The construction of relevant prognostic models helps to stratify the prognosis of surgically treated patients and guide personalized clinical decision-making, thereby improving patient survival rates. Currently, the prognostic assessment of HCC is based on several commonly used staging systems, such as Tumor-Node-Metastasis (TNM), Cancer of the Liver Italian Program (CLIP), and Barcelona Clinic Liver Cancer (BCLC). Given the insufficiency of these staging systems and the aim to improve the accuracy of prognostic prediction, researchers have incorporated further prognostic factors, such as microvascular infiltration, and proposed some new prognostic models for HCC. To provide insights into the prospects of clinical oncology research, this review describes the commonly used HCC staging systems and new models proposed in recent years.

Study on risk factors of abnormal pulmonary function among dust-exposed workers and prediction model / 中华劳动卫生职业病杂志

Objective: To explore the influencing factors of abnormal pulmonary function in dust-exposed workers and establish the risk prediction model of abnormal pulmonary function. Methods: In April 2021, a total of 4255 dust exposed workers from 47 enterprises in 2020 were included in the study. logistic regression was used to analyze the influencing factors of abnormal pulmonary function in dust-exposed workers, and the corresponding nomogram prediction model was established. The model was evaluated by ROC curve, Calibrationpolt and decision analysis curve. Results: logistic regression analysis showed that age (OR=1.03, 95%CI=1.02~1.05, P<0.001) , physical examination type (OR=4.52, 95%CI=1.69~12.10, P=0.003) , dust type (Comparison with coal dust, Cement dust, OR=3.45, 95%CI=1.45~8.18, P=0.005, Silica dust (OR=2.25, 95%CI=1.01~5.03, P=0.049) , blood pressure (OR=1.63, 95%CI=1.22~2.18, P=0.001) , creatinine (OR=0.08, 95%CI=0.05~0.12, P<0.001) , daily exposure time (OR=1.06, 95%CI=1.10~1.12, P=0.034) and total dust concentration (OR=1.29, 95%CI=1.08~1.54, P=0.005) were the influencing factors of abnormal pulmonary function. The area under the ROC curve of risk prediction nomogram model was 0.764. The results of decision analysis curve showed that the nomogram model had reference value in the prevention and intervention of abnormal pulmonary function when the threshold probability exceeded 0.05. Conclusion: The accuracy ofthe nomogram model constructed by logistic regression werewell in predicting the risk of abnormal lung function of dust-exposed workers.